Machine vision as input to a cmp process control algorithm

ABSTRACT

During chemical mechanical polishing of a substrate, a signal value that depends on a thickness of a layer in a measurement spot on a substrate undergoing polishing is determined by a first in-situ monitoring system. An image of at least the measurement spot of the substrate is generated by a second in-situ imaging system. Machine vision processing, e.g., a convolutional neural network, is used to determine a characterizing value for the measurement spot based on the image. Then a measurement value is calculated based on both the characterizing value and the signal value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/735,772, filed Sep. 24, 2018, the disclosure of which isincorporated by reference.

TECHNICAL FIELD

The present disclosure relates to optical monitoring of a substrate,e.g., during processing such as chemical mechanical polishing.

BACKGROUND

An integrated circuit is typically formed on a substrate by thesequential deposition of conductive, semiconductive, or insulativelayers on a silicon wafer. One fabrication step involves depositing afiller layer over a non-planar surface and planarizing the filler layer.For some applications, the filler layer is planarized until the topsurface of a patterned layer is exposed. For example, a conductivefiller layer can be deposited on a patterned insulative layer to fillthe trenches or holes in the insulative layer. After planarization, theportions of the conductive layer remaining between the raised pattern ofthe insulative layer form vias, plugs, and lines that provide conductivepaths between thin film circuits on the substrate. For otherapplications, the filler layer is planarized until a predeterminedthickness is left over an underlying layer. For example, a dielectriclayer deposited can be planarized for photolithography.

Chemical mechanical polishing (CMP) is one accepted method ofplanarization. This planarization method typically requires that thesubstrate be mounted on a carrier head. The exposed surface of thesubstrate is typically placed against a rotating polishing pad with adurable roughened surface. The carrier head provides a controllable loadon the substrate to push it against the polishing pad. A polishingliquid, such as a slurry with abrasive particles, is typically suppliedto the surface of the polishing pad.

One problem in CMP is using an appropriate polishing rate to achieve adesirable profile, e.g., a substrate layer that has been planarized to adesired flatness or thickness, or a desired amount of material has beenremoved. Variations in the initial thickness of a substrate layer, theslurry distribution, the polishing pad condition, the relative speedbetween the polishing pad and a substrate, and the load on a substratecan cause variations in the material removal rate across a substrate,and from substrate to substrate. These variations cause variations inthe time needed to reach the polishing endpoint and the amount removed.Therefore, it may not be possible to determine the polishing endpointmerely as a function of the polishing time, or to achieve a desiredprofile merely by applying a constant pressure.

In some systems, a substrate is monitored in-situ during polishing,e.g., by an optical monitoring system or eddy current monitoring system.Thickness measurements from the in-situ monitoring system can be used toadjust pressure applied to the substrate to adjust the polishing rateand reduce within-wafer non-uniformity (WIWNU).

SUMMARY

A polishing system includes a support to hold a polishing pad, a carrierhead to hold a substrate in contact with the polishing pad, a motor togenerate relative motion between the support and the carrier head, afirst in-situ monitoring system to generate a signal that depends on athickness of a layer in a measurement spot on the substrate, a secondin-situ imaging system to generate an image of at least the measurementspot of the substrate at substantially the same time as the in-situmonitoring system generates the signal for the measurement spot on thesubstrate, and a controller. The controller is configured to receive theimage from the second in-situ imaging system and determine acharacterizing value for the measurement spot based on the image usingmachine vision processing, receive the signal from the in-situmonitoring system, generate a measurement value based on both thecharacterizing value and the signal value, and at least one of haltpolishing of the substrate or adjust a polishing parameter based on themeasurement value.

In another aspect, a computer program product for controlling processingof a substrate includes instructions for causing one or more processorsto receive from a first in-situ monitoring system a signal value thatdepends on a thickness of a layer in a measurement spot on a substrateundergoing polishing, receive image data for at least the measurementspot of the substrate from a second in-situ imaging system, determine acharacterizing value for the measurement spot based on the image usingmachine vision processing, generate a measurement value based on boththe characterizing value and the signal value, and at least one of haltpolishing of the substrate or adjust a polishing parameter based on themeasurement value.

Implementations may include one or more of the following features.

The machine vision processing may include processing the image with anartificial neural network. The artificial neural network may be aconvolutional neural network. The controller may be configured to trainthe artificial neural network by backpropagation using training dataincluding images and known characterizing values for the images.

The first in-situ monitoring system may include a spectrographicmonitoring system to generate a measured spectrum for the measurementspot. The artificial neural network may be is configured to determine aclassification of a portion of the substrate corresponding to themeasurement spot. The classification may correspond to a type ofstructure on the substrate. The type of structure may include at leastone of an array, a scribe line, a periphery, and a contact pad. One of aplurality of libraries of reference spectra may be selected based on theclassification.

The first in-situ monitoring system may include an eddy currentmonitoring system to generate a signal value for the measurement spot.The artificial neural network may be configured to determine a geometryvalue for a feature that affects current flow in the measurement spot.The geometry value may include at least one of a distance, size ororientation.

A portion of the image data corresponding to the measurement spot may bedetermined. Image data from the second in-situ imaging system may besynchronized with the signal from the first in-situ monitoring system.

Certain implementations may have one or more of the followingadvantages. Process control techniques can target the performancesensitive portions of a die. The thickness of a layer on a substrate canbe measured more accurately and/or more quickly. Within-wafer thicknessnon-uniformity and wafer-to-wafer thickness non-uniformity (WIWNU andWTWNU) may be reduced, and reliability of an endpoint system to detect adesired processing endpoint may be improved. Post CMP metrics can bebased on on yield and/or performance sensitive portions of products, asopposed to average die thickness (which may include areas of the diethat are irrelevant to product performance).

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic cross-sectional view of an example of a polishingapparatus.

FIG. 2A is a schematic illustration of an in-situ optical monitoringsystem.

FIG. 2B is a schematic illustration of an in-situ eddy currentmonitoring system.

FIG. 3 is a schematic top view of the polishing apparatus.

FIG. 4 illustrates a schematic illustration of a line scan imagingsystem.

FIG. 5 illustrates a neural network used as part of the controller forthe polishing apparatus.

FIG. 6 illustrates a graph of measurement values over time.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Various techniques, e.g., eddy current monitoring and opticalmonitoring, can be used to monitor a substrate during processing. Suchmonitoring techniques can proceed in a two stage manner. First, the rawsignal from the monitoring system, e.g., a measured spectrum from aspectrophotometer or a voltage from an eddy current monitoring system,is converted to a more useful form of measurement, e.g., an indexrepresenting progress through polishing or a thickness value. Thesequence of measurements over time as processing progresses can then bemonitored for use in process control. For example, a function can be fitto the sequence of measurements, and the time at which the function isprojected to reach a threshold value can be used to trigger thepolishing endpoint or to control other polishing parameters.

If a sensor of the monitoring system sweeps across the substrate,measurements can made at different positions on the substrate.Consequently, the measurements can be made at different regions of thesubstrate, e.g., within a die versus in a scribe line, or at differentregions within a die, e.g., an array, a contact pad, etc. Thesedifferent regions can have different properties and provide differentraw signals. It would be useful to determine the type of region in whicha measurement is made in order to properly convert the raw signal into auseful measurement.

Although radial positions of the measurements can be determined, e.g.,due to rotational slippage of the substrate relative to carrier head,the angular position of a measurement on the substrate may not be knownat all. Consequently, commercialized in-situ monitoring techniques havenot taken into account where in a die the measurement is made whenconverting the raw signal into a useful measurement.

Moreover, although some monitoring systems perform filtering to rejectsome raw signals, e.g., rejecting a spectrum based on the shape of thespectrum, such techniques do not use information from the surroundingportion of the substrate.

However, images collected by an in-situ imager can be processed by amachine learning technique, e.g., a convolutional neural network, todetermine a characteristic of the substrate where a measurement is beingperformed by another monitoring system. This characteristic can be, forexample, the type of region, e.g., scribe line, array, periphery, wherethe measurement is being made, or a relative orientation and/or distanceof various features, e.g., guard rings, to the location of themeasurement. The characteristic can then be fed as an input to thein-situ monitoring system to influence the conversion of the raw signalto the measurement.

FIG. 1 illustrates an example of a polishing apparatus 20. The polishingapparatus 20 can include a rotatable disk-shaped platen 22 on which apolishing pad 30 is situated. The platen is operable to rotate about anaxis 23. For example, a motor 24 can turn a drive shaft 26 to rotate theplaten 22.

The polishing pad 30 can be detachably secured to the platen 22, forexample, by a layer of adhesive. The polishing pad 30 can be a two-layerpolishing pad with an outer polishing layer 32 and a softer backinglayer 34. A window 36 can be formed in the polishing pad 30.

The polishing apparatus 20 can include a polishing liquid supply port 40to dispense a polishing liquid 42, such as an abrasive slurry, onto thepolishing pad 30. The polishing apparatus 20 can also include apolishing pad conditioner to abrade the polishing pad 30 to maintain thepolishing pad 30 in a consistent abrasive state.

A carrier head 50 is operable to hold a substrate 10 against thepolishing pad 30. Each carrier head 50 also includes a plurality ofindependently controllable pressurizable chambers, e.g., three chambers52 a-52 c, which can apply independently controllable pressurizes toassociated zones on the substrate 10. The center zone on the substratecan be substantially circular, and the remaining zones can be concentricannular zones around the center zone.

The chambers 52 a-52 c can be defined by a flexible membrane 54 having abottom surface to which the substrate 10 is mounted. The carrier head 50can also include a retaining ring 56 to retain the substrate 10 belowthe flexible membrane 54. Although only three chambers are illustratedin FIG. 1 for ease of illustration, there could be a single chamber, twochambers, or four or more chambers, e.g., five chambers. In addition,other mechanisms to adjust the pressure applied to the substrate, e.g.,piezoelectric actuators, could be used in the carrier head 50.

Each carrier head 50 is suspended from a support structure 60, e.g., acarousel or track, and is connected by a drive shaft 62 to a carrierhead rotation motor 64 so that the carrier head can rotate about an axis51. Optionally each carrier head 50 can oscillate laterally, e.g., onsliders on the carousel, by motion along or track; or by rotationaloscillation of the carousel itself. In operation, the platen 22 isrotated about its central axis 23, and the carrier head 50 is rotatedabout its central axis 51 and translated laterally across the topsurface of the polishing pad 30.

The polishing apparatus also includes a first in-situ monitoring system100, and a second in-situ imaging system 150. Together, the in-situmonitoring system 150 and the in-situ imaging system 150 can be used tocontrol the polishing parameters, e.g., the pressure in one or more ofthe chambers 52 a-52 c, and/or to detect a polishing endpoint and haltpolishing.

The first in-situ monitoring system 100 includes a sensor 100 a (seeFIG. 3) that generates a raw signal that depends on the thickness of thelayer being polished. The first in-situ monitoring system 100 can be,for example, an eddy current monitoring system or an optical monitoringsystem, e.g., a spectrographic monitoring system.

The sensor can be configured to sweep across the substrate. For example,the sensor can be secured to and rotate with the platen 22 such that thesensor sweeps in arc across the substrate with each rotation of theplaten.

Referring to FIG. 2A, as an optical monitoring system, the first in-situmonitoring system 100 can include a light source 102, a light detector104, and circuitry 106 for sending and receiving signals between acontroller 90, e.g., a computer, and the light source 102 and lightdetector 104. One or more optical fibers can be used to transmit thelight from the light source 102 to the window 36, and to transmit lightreflected from the substrate 10 to the detector 104. For example, abifurcated optical fiber 108 can be used to transmit the light from thelight source 102 to the window 36 and back to the detector 104. In thisimplementation, an end of the bifurcated fiber 108 can provide thesensor that sweeps across the substrate. If the optical monitoringsystem is a spectrographic system, the light source 102 can be operableto emit white light and the detector 104 can be a spectrometer.

Referring to FIG. 2B, as an eddy current monitoring system, the firstin-situ monitoring system 100 can include a magnetic core 112 and atleast one coil 114 wound around a portion of the core 114. Drive andsense circuitry 116 is electrically connected to the coil 114. The driveand sense circuitry 116 can apply an AC current to the coil 114, whichgenerates a magnetic field between two poles of the core 112 that canpass into the substrate 10. In this implementation, the core 112 andcoil 114 can provide the sensor that sweeps across the substrate. Thecircuitry 116 can include a capacitor connected in parallel with thecoil 114. Together the coil 114 and the capacitor can form an LCresonant tank. When the magnetic field reaches a conductive layer, themagnetic field 150 can pass through and generate a current (if the layeris a loop) or create an eddy-current (if the layer is a sheet). Thismodifies the effective impedance of the LC circuit. The drive and sensecircuitry 116 can detect the change in effective impedance, and generatea signal that can be sent to the controller 90.

In either case, the output of the circuitry 106 or 116 can be a digitalelectronic signal that passes through a rotary coupler 28, e.g., a slipring, in the drive shaft 26 to the controller 90 (see FIG. 1).Alternatively, the circuitry 106 or 116 could communicate with thecontroller 90 by a wireless signal. Some or all of the circuitry 106 or116 can be installed in the platen 22.

The controller 90 can be a computing device that includes amicroprocessor, memory and input/output circuitry, e.g., a programmablecomputer. Although illustrated with a single block, the controller 90can be a networked system with functions distributed across multiplecomputers.

As the controller 90 can perform a portion of the processing of thesignal, e.g., conversion of the “raw” signal to the usable measurement,the controller 90 can be considered to provide a portion of the firstmonitoring system.

As shown by in FIG. 3, due to the rotation of the platen (shown by arrowA), as the sensor 100 a travels below the carrier head, the firstin-situ monitoring system makes measurements at a sampling frequency. Asa result, the measurements are taken at locations 94 in an arc thattraverses the substrate 10 (the number of points is illustrative; moreor fewer measurements can be taken than illustrated, depending on thesampling frequency). The substrate can also be rotating (shown by arrowB) and oscillating radially (shown by arrow C).

The polishing system 20 can include a position sensor 96, such as anoptical interrupter, to sense when the sensor 100 a of the first in-situmonitoring system 100 is underneath the substrate 10 and when the sensor100 a is off the substrate 10. For example, the position sensor 96 canbe mounted at a fixed location opposite the carrier head 70. A flag 98can be attached to the periphery of the platen 22. The point ofattachment and length of the flag 98 is selected so that it can signalthe position sensor 96 when the sensor 100 a sweeps underneath thesubstrate 10.

Alternately or in addition, the polishing system 20 can include anencoder to determine the angular position of the platen 22.

Over one rotation of the platen, spectra are obtained from differentpositions on the substrate 10. In particular, some spectra can beobtained from locations closer to the center of the substrate 10 andsome can be obtained from locations closer to the edge. The controller90 can be configured to calculate a radial position (relative to thecenter of the substrate 10) for each measurement from a scan based ontiming, motor encoder information, platen rotation or position sensordata, and/or optical detection of the edge of the substrate and/orretaining ring. The controller can thus associate the variousmeasurements with the various zones on the substrate. In someimplementations, the time of measurement of can be used as a substitutefor the exact calculation of the radial position.

The in-situ imaging system 150 is positioned to generate an image ofsubstantially the same portion of the substrate 10 that the firstin-situ monitoring system 100 is measuring. In short, the camera of theimaging system is co-located with the sensor of the in-situ monitoringsystem 100.

Referring to FIG. 4, the in-situ imaging system 150 can include a lightsource 152, a light detector 154, and circuitry 156 for sending andreceiving signals between the controller 90 and the light source 152 andlight detector 154.

The light source 152 can be operable to emit white light. In oneimplementation, the white light emitted includes light havingwavelengths of 200-800 nanometers. A suitable light source is an arrayof white-light light emitting diodes (LEDs), or a xenon lamp or a xenonmercury lamp. The light source 152 is oriented to direct light 158 ontothe exposed surface of the substrate 10 at a non-zero angle of incidencea. The angle of incidence a can be, for example, about 30° to 75°, e.g.,50°.

The light source 152 can illuminate a substantially linear elongatedregion. The elongated region can span the width of the substrate 10. Thelight source can 152 can include optics, e.g., a beam expander, tospread the light from the light source into an elongated region.Alternatively or in addition, the light source 152 can include a lineararray of light sources. The light source 152 itself, and the regionilluminated on the substrate, can be elongated and have a longitudinalaxis parallel to the surface of the substrate.

A diffuser 160 can be placed in the path of the light 168, or the lightsource 162 can include a diffuser, to diffuse the light before itreaches the substrate 10.

The detector 154 is a camera, e.g., a color camera, that is sensitive tolight from the light source 152. The camera includes an array ofdetector elements. For example, the camera can include a CCD array. Insome implementations, the array is a single row of detector elements.For example, the camera can be a linescan camera. The row of detectorelements can extend parallel to the longitudinal axis of the elongatedregion illuminated by the light source 152. Where the light source 165includes a row of light emitting elements, the row of detector elementscan extend along a first axis parallel to the longitudinal axis of thelight source 152. A row of detector elements can include 1024 or moreelements.

The detector 154 is configured with appropriate focusing optics 162 toproject a field of view of the substrate onto the array of detectorelements of the detector 154. The field of view can be long enough toview the entire width of the substrate 10, e.g., 150 to 300 mm long. Thedetector 164 can be also be configured such that the pixel width iscomparable to the pixel length. For example, an advantage of a linescancamera is its very fast frame rate. The frame rate can be at least 5kHz. The frame rate can be set at a frequency such that as the imagedarea scans across the substrate 10, the pixel width is comparable to thepixel length, e.g., equal to or less than about 0.3 mm.

The light source 162 and the light detector 164 can be supported in arecess in the platen, e.g., the same recess that holds the sensor of thefirst in-situ monitoring system 100.

A possible advantage of having a line-scan camera and light source thatmove together across the substrate is that, e.g., as compared to aconventional 2D camera, the relative angle between the light source andthe camera remains constant for different positions across the wafer.Consequently, artifacts caused by variation in the viewing angle can bereduced or eliminated. In addition, a line scan camera can eliminateperspective distortion, whereas a conventional 2D camera exhibitsinherent perspective distortion, which then needs to be corrected by animage transformation.

Optionally a polarizing filter 164 can be positioned in the path of thelight, e.g., between the substrate 10 and the detector 154. Thepolarizing filter 164 can be a circular polarizer (CPL). A typical CPLis a combination of a linear polarizer and quarter wave plate. Properorientation of the polarizing axis of the polarizing filter 164 canreduce haze in the image and sharpen or enhance desirable visualfeatures.

The controller 90 assembles the individual image lines from the lightdetector 154 into a two-dimensional image. The camera 164 can be a colorcamera with separate detector elements, e.g., for each of red, blue andgreen, in which case the controller 90 assembles the individual imagelines from the light detector 154 into a two-dimensional color image.The two-dimensional color image can include a monochromatic image 204,206, 208 for each color channel, e.g., for each of the red, blue andgreen color channels.

Referring to FIG. 5, the controller 90 can convert the raw signal fromthe in-situ monitoring system into a useful measurement. The controller90 uses both the signal from the first in-situ monitoring system 100 andimage data from the second in-situ imaging system 150 to calculate themeasurement. The images collected from the in-situ imaging system 150can be synchronized with the data stream collected from the firstin-situ monitoring system 100

In particular, the controller 90 feeds the image from the in-situimaging system 150 into a machine vision system 200 that is configuredto derive a characterizing value for the portion of substrate beingmeasured by the first in-situ monitoring system 100. The machine visionsystem can include, for example, a neural network 210. The neuralnetwork 210 can be a convolutional neural network.

The neural network 210 includes a plurality of input nodes 212, e.g., aninput node 212 for each pixel in the image from the in-situ imagingsystem 150. These can include input nodes N₁, N₂ . . . N_(L). The neuralnetwork 210 also includes a plurality of hidden nodes 214 (also called“intermediate nodes” below), and at least one output node 216 that willgenerate at least one characterizing value.

In general, a hidden node 214 outputs a value that a non-linear functionof a weighted sum of the values from the nodes to which the hidden nodeis connected.

For example, the output of a hidden node 214, designated node k, can beexpressed as:

tan h(0.5*a _(k1)(I ₁)+a _(k2)(I ₂)+ . . . +a _(kM)(I _(M))+b _(k))  Equation 1

where tan h is the hyperbolic tangent, a is a weight for the connectionbetween the k^(th) intermediate node and the x^(th) input node (out of Minput nodes), and I_(M) is the value at the M^(th) input node. However,other non-linear functions can be used instead of tan h, such as arectified linear unit (ReLU) function and its variants.

The architecture of the neural network 210 can vary in depth and width.Although the neural network 210 is shown with a single column ofintermediate nodes 214, a practical matter the neural network wouldinclude many columns, which could have various kinds of connections. Theconvolutional neural network can perform multiple iterations ofconvolution and pooling, followed by classification.

The neural network 210 can be trained, e.g., in a training mode usingbackpropagation with sample images and sample characterizing values.Thus, in operation, the machine vision system 200 generates acharacterizing value based on the image from the in-situ imaging system150. This can be performed for each value of the “raw signal” receivedfrom the in-situ monitoring system 100.

A raw signal from the in-situ monitoring system 100 and thecharacterizing value that is synchronized with the raw signal (e.g.,corresponding to the same spot on the substrate), are input into aconversion algorithm module 220. The conversion algorithm module 220calculates a measurement value on the characterizing value and the rawsignal.

The measurement value is typically the thickness of the outer layer, butcan be a related characteristic such as thickness removed. In addition,the measurement value can be a more generic representation of theprogress of the substrate through the polishing process, e.g., an indexvalue representing the time or number of platen rotations at which themeasurement would be expected to be observed in a polishing process thatfollows a predetermined progress.

The measurement value can be fed to process control sub-system 240 toadjust the polishing process, e.g., detect a polishing endpoint and haltpolishing and/or adjust polishing pressures during the polishing processto reduce polishing non-uniformity, based on the series ofcharacterizing values. The process control module 240 can outputprocessing parameters, e.g., a pressure for a chamber in the carrierhead and/or a signal to halt polishing.

For example, referring to FIG. 6, a first function 254 can be fit to thesequence 250 of measurement values 252 for a first zone, and a secondfunction 264 can be fit to the sequence 260 of characteristic values 262for a second zone. The process controller 240 can calculate the times T1and T2 at which the first and second functions are projected to reach atarget value V, and calculate an adjusted processing parameter, e.g., anadjusted carrier head pressure, that will cause one of the zones to bepolished at a revised rate (shown by line 270) such that the zones reachthe target at approximately the same time.

A polishing endpoint can be triggered by the process controller 240 atthe time that a function indicates the characteristic values reaches thetarget value V.

In some implementations, multiple measurement values can be combined,either at the conversion algorithm module 220 or the process controlmodule 240. For example, if the system generates multiple measurementvalues from a single scan of the sensor across the substrate, theconversion algorithm module 220 could combine multiple measurements fromthe single scan to generate either a single measurement per scan or asingle measurement per radial zone on the substrate. However, in someimplementations, a measurement value is generated for each location 94for which the sensor 100 a generates a raw signal value (see FIG. 3).

In some implementations, the neural network 210 generates multiplecharacterizing values at multiple output nodes 216. The additionalcharacterizing value(s), i.e., beyond the characterizing value thatrepresents a thickness measurement, can represent other characteristicsof the substrate, e.g., wafer orientation, type of structures (e.g.,memory array, central processing units) on the wafer. The additionalcharacterizing value(s) can be fed into the process control 240.

EXAMPLE 1

The in-situ monitoring system 100 can be a spectrographic monitoringsystem. The same window 36 can by the sensor of the spectrographicmonitoring system and the in-situ imaging system 150. A window of datafrom the line scan camera of the in-situ imaging system 150 that iscentered around the time of acquisition of the spectrum by the in-situmonitoring system 100 can be used to reconstruct a two dimensional imageof the portion of the substrate 10 from which the spectrum wascollected.

The machine vision system 200 can include a convolutional neural network(CNN) 210. To train the neural network 210, a series of the images fromone or more reference substrates can be manually identified with arelevant class (e.g., array, scribe line, periphery, contact pad, etc.).Assigning a classification to the image is sometimes termed“annotation.” The images and the classes from the reference substratescan then be input to the neural network in a training mode, e.g., usingbackpropagation, to train the neural network 210 as an image classifier.Note that such image classifiers can be trained with a relatively smallnumber of annotated images via the use of transfer learning in which apre-trained image classification network is shown a few additionalimages from a new domain.

In operation, during polishing of product substrates, the images are fedinto the neural network 210. The output of the neural network 210 isused in real-time to associate each measured spectrum with aclassification of the portion of the substrate from which the spectrumwas obtained.

The image classification by the convolutional neural network can beconcatenated with the measured spectrum before be being fed into anothermodel which is used for thickness estimation or prediction.

The classification can be used by the conversion algorithm module 220.For example, the controller 90 may store a plurality of libraries ofreference spectra with each reference spectrum having an associatedmeasurement value, e.g., an index value. The controller 90 can selectone of the libraries based on the classification received from theneural network 210. Then the reference system from the selected librarythat best matches the measured spectrum can be determined, e.g., byfinding the reference spectrum with the smallest sum of squareddifferences relative to the measured spectrum. The index value for thebest-matching reference spectrum can then be used as the measurementvalue.

EXAMPLE 2

The in-situ monitoring system 100 can be an eddy current monitoringsystem. The sensor 100 a of the eddy current monitoring system andsensor of the in-situ imaging system 150 are co-located, e.g.,positioned in the same recess in the platen. The line scan camera of thein-situ imaging system 150 generates a time synchronized image thatcovers the entire sweep of the sensor 100 a across the substrate.

The machine vision system 200 can include a convolutional neural network(CNN) 210. To train the neural network 210, the geometry (e.g.,position, size and/or orientation) of substrate features that effectcurrent flow (e.g., a guard ring) can be manually identified. The imagesand the geometry values from the reference substrates can then be inputto the neural network in a training mode, e.g., using backpropagation,to train the neural network 210 as a feature geometry reconstructor.

In operation, during polishing of product substrates, the images are fedinto the neural network 210. The output of the neural network 210 isused in real-time to associate each measured value from the eddy currentmonitoring system with a geometry value for the portion of the substratefrom which the spectrum was obtained.

The geometry values generated by the neural network 210 can be used bythe conversion algorithm module 220. A map from eddy current signal toresistance is dependent on the relative orientation and location offeatures on the substrate. For example, a sensitivity of the sensor 100a to a conductive loop on the substrate can depend on an orientation ofthe loop. The controller 90 may include a function that calculates again based on the geometry value, e.g., the orientation. This gain canthen be applied to the signal, e.g., the signal value can be multipliedby the gain. Thus, the geometry value can be used to adjust how the eddycurrent sensor data is interpreted.

Conclusion

Embodiments of the invention and all of the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructural means disclosed in this specification and structuralequivalents thereof, or in combinations of them. Embodiments of theinvention can be implemented as one or more computer program products,i.e., one or more computer programs tangibly embodied in amachine-readable storage media, for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor,a computer, or multiple processors or computers. A computer program(also known as a program, software, software application, or code) canbe written in any form of programming language, including compiled orinterpreted languages, and it can be deployed in any form, including asa stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing environment. A computer programdoes not necessarily correspond to a file. A program can be stored in aportion of a file that holds other programs or data, in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub-programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers at one site or distributed acrossmultiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

The above described polishing apparatus and methods can be applied in avariety of polishing systems. Either the polishing pad, or the carrierheads, or both can move to provide relative motion between the polishingsurface and the substrate. For example, the platen may orbit rather thanrotate. The polishing pad can be a circular (or some other shape) padsecured to the platen. The polishing system can be a linear polishingsystem, e.g., where the polishing pad is a continuous or a reel-to-reelbelt that moves linearly. The polishing layer can be a standard (forexample, polyurethane with or without fillers) polishing material, asoft material, or a fixed-abrasive material. Terms of relativepositioning are used relative orientation or positioning of thecomponents; it should be understood that the polishing surface andsubstrate can be held in a vertical orientation or some otherorientation with respect to gravity.

Although the description above has focused on chemical mechanicalpolishing, the control system can be adapted to other semiconductorprocessing techniques, e.g., etching or deposition, e.g., chemical vapordeposition. Rather than a line scan camera, a camera that images atwo-dimensional region of substrate could be used. In this case,multiple images may need to be combined.

Particular embodiments of the invention have been described. Otherembodiments are within the scope of the following claims.

What is claimed is:
 1. A polishing system, comprising: a support to holda polishing pad; a carrier head to hold a substrate in contact with thepolishing pad; a motor to generate relative motion between the supportand the carrier head; a first in-situ monitoring system to generate asignal that depends on a thickness of a layer in a measurement spot onthe substrate; a second in-situ imaging system to generate an image ofat least the measurement spot of the substrate at substantially the sametime as the in-situ monitoring system generates the signal for themeasurement spot on the substrate; and a controller configured toreceive the image from the second in-situ imaging system and determine acharacterizing value for the measurement spot based on the image usingmachine vision processing, receive the signal from the in-situmonitoring system, generate a measurement value based on both thecharacterizing value and the signal value, and at least one of haltpolishing of the substrate or adjust a polishing parameter based on themeasurement value.
 2. The system of claim 1, wherein the machine visionprocessing comprises an artificial neural network.
 3. The system ofclaim 2, wherein the machine vision processing comprises a convolutionalneural network.
 4. The system of claim 1, wherein the controller isconfigured to train the artificial neural network by backpropagationusing training data including images and known characterizing values forthe images.
 5. The system of claim 2, wherein the first in-situmonitoring system comprises a spectrographic monitoring system togenerate a measured spectrum for the measurement spot.
 6. The system ofclaim 5, wherein the artificial neural network is configured todetermine a classification of a portion of the substrate correspondingto the measurement spot, the classification corresponding to a type ofstructure on the substrate.
 7. The system of claim 6, wherein the typeof structure includes at least one of an array, a scribe line, aperiphery, and a contact pad.
 8. The system of claim 2, wherein thefirst in-situ monitoring system comprises an eddy current monitoringsystem to generate a signal value for the measurement spot.
 9. Thesystem of claim 8, wherein the artificial neural network is configuredto determine a geometry value for a feature that affects current flow inthe measurement spot.
 10. The system of claim 9, wherein the geometryvalue includes at least one of a distance, size or orientation.
 11. Thesystem of claim 1, wherein the controller is configured to determine aplurality of characterizing values for a plurality of differentcharacteristics of the substrate at the measurement spot based on theimage using machine vision processing.
 12. A computer program productfor controlling processing of a substrate, the compute program producttangibly embodied in a non-transitory computer readable media andcomprising instructions for causing a processor to: receive, from afirst in-situ monitoring system, a signal value that depends on athickness of a layer in a measurement spot on a substrate undergoingpolishing; receive image data for at least the measurement spot of thesubstrate from a second in-situ imaging system; determine acharacterizing value for the measurement spot based on the image usingmachine vision processing; generate a measurement value based on boththe characterizing value and the signal value, and at least one of haltpolishing of the substrate or adjust a polishing parameter based on themeasurement value.
 13. The computer program product of claim 12,comprising instructions to determine a portion of the image datacorresponding to the measurement spot.
 14. The computer program productof claim 12, comprising instructions to synchronize image data from thesecond in-situ imaging system with signal values from the first in-situmonitoring system.
 15. The computer program product of claim 12, whereinmachine vision processing comprises feeding the image data to anartificial neural network.
 16. The computer program product of claim 14,wherein the artificial neural network comprises a convolutional neuralnetwork.
 17. The computer program product of claim 12, wherein theinstructions to perform machine vision processing comprise instructionsto determine a classification of a portion of the substratecorresponding to the measurement spot.
 18. The computer program productof claim 17, wherein the signal comprises a measured spectrum, and theinstructions to generate the measurement value comprise instructions toselect one of a plurality of libraries of reference spectra based on theclassification.
 19. The computer program product of claim 12, whereinthe instructions to perform machine vision processing compriseinstructions to determine a geometry value of a feature in a portion ofthe substrate corresponding to the measurement spot.
 20. The computerprogram product of claim 18, wherein the signal comprises a signal valuefrom an eddy current monitoring system, and the geometry value comprisesan orientation of the feature.